Advanced Microsamples: History
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Contributor:
Jayden Lee Roberts
,
Luke Whiley
,
Nicola Gray
,
Melvin Gay
,
Nathan G Lawler

Although the application of microsamples in metabolic phenotyping exists, it is still in its infancy, with whole blood being overwhelmingly the primary biofluid collected through the collection method of dried blood spots. Research into the metabolic phenotyping of microsamples is limited; however, with advances in commercially available microsampling devices, common barriers such as volumetric inaccuracies and the ‘haematocrit effect’ in dried blood spot microsampling can be overcome.

  • gas chromatography–mass spectrometry (GC–MS)
  • liquid chromatography–mass spectrometry (LC–MS)

1. Advanced Microsamples: Current Applications in Metabolic Phenotyping

Recently, the implementation of advanced microsampling devices in metabolic phenotyping workflows has been reported in the literature; however, collection and analytical protocols have not been extensively evaluated and optimised for the given devices available. These reported advanced microsampling devices cover a range of technologies for capillary blood collection, including advanced dried samples, passive separation devices, and whole biofluid collectors (Table 1). However, by no means do they encompass all devices currently available, commercially or for research purposes. Moreover, although the literature presents a large variety of applications and pre-analytical sample preparation techniques, all studies primarily utilised LC–MS based platforms for analysis.
Table 1. Comparison of microsamples in mass spectrometry-based analytical chemistry and metabolic phenotyping methodologies: Extraction and analytical methods.
Microsampling Technology Extraction Solvent Metabolic Phenotyping Method Title Ref.
Traditional dried samples
(DBS)		 MeOH Targeted DI-HRMS for amino acids, ketones, lipids
Untargeted DI-HRMS		 Understanding acute metabolic decompensation in propionic and methylmalonic acidemias: A deep metabolic phenotyping approach [1]
Traditional dried samples
(DBS)		 MeOH UPLC–MS/MS
TQ, BEH C18 column
Targeted analysis of anticancer drug, cyclophosphamide, metabolites		 Phenotyping study of cyclophosphamide 4-hydroxylation in Malay cancer patients [2]
Traditional dried samples
(DBS)		 MeOH:H2O (4:1, v/v)
2% formic acid		 Untargeted UHPLC–qTOF–MS
RP and HILIC separation		 A feasibility study of metabolic phenotyping of dried blood spot specimens in rural Chinese women exposed to household air pollution [3]
Traditional dried samples
(DBS)		 100 µL of MilliQ H2O
250 µL of MeOH
500 µL of MTBE		 Orbitrap—DI-HRMS
Lipidomics		 The development and validation of a fast and robust dried blood spot based lipid profiling method to study infant metabolism [4]
Traditional dried samples
(DBS, DPS)		 MeOH 7890A GC System coupled to 5975 inert MSD with Triple-Axis Detector Evaluation of dried blood spots as sample matrix for gas chromatography/mass spectrometry based metabolomic profiling [5]
Traditional dried samples
(DBS, DUS)		 DBS: MeOH:H2O (4:1, v/v)
DUS: MeOH:H2O (8:1, v/v)		 UHPLC–MS
RP, HILIC, lipid methods		 Investigation of the 12-month stability of dried blood and urine spots applying untargeted UHPLC–MS metabolomic assays [6]
Traditional dried samples
(DBS)		 ACN/MeOH with H2O at a ratio of 2:1–4:1 LC–MS/MS
Drug, lipid, acylcarnitine analyses		 Drug, lipid, and acylcarnitine profiling using dried blood spot (DBS) technology in drug discovery [7]
Traditional dried samples
(DBS)		 Not specified: “relatively uncomplicated sample extraction steps” DI-HRMS Untargeted metabolic profiling in dried blood spots identifies disease fingerprint for pyruvate kinase deficiency [8]
Traditional dried samples
(DBS)		 MeOH:ACN (1:1) LC–MS Nontargeted mass spectrometry of dried blood spots for interrogation of the human circulating metabolome [9]
Traditional dried samples
(DBS)		 25% (v/v) aqueous MeOH UPLC–MS Metabolite profiles from dried blood spots for metabonomic studies using UPLC combined with orthogonal acceleration ToF-MS: effects of different papers and sample storage stability [10]
Traditional dried samples
(DBS)
Advanced dried samples
(HemaXis DB10, TASSO-M20)		 MeOH:H2O (95:5, v/v) UHPLC–MS/MS
MRM of steroid hormones		 Steroid profiling by UHPLC–MS/MS in dried blood spots collected from healthy women with and without testosterone gel administration [11]
Advanced dried samples
(VAMS)		 ACN:H2O (60:40, v/v) UHPLC–MS/MS for organic acids
HILIC–MS/MS for amino acids		 Targeted metabolomics of whole blood using volumetric absorptive microsampling [12]
Advanced dried samples
(VAMS)		 ACN:H2O (70:30, v/v) UHPLC–qToF–MS
HILIC
Untargeted analysis		 Pre-analytical evaluation of volumetric absorptive microsampling and integration in a mass spectrometry-based metabolomics workflow [13]
Advanced dried samples
(hemaPEN)		 ACN:H2O (60:40, v/v) UHPLC–MS/MS
Organic acid, amino acid methods		 Blood microsampling to monitor metabolic profiles during physical exercise [14]
Advanced dried samples
(hemaPEN)		 MeOH:H2O (80:20, v/v)
0.01% formic acid		 LC–MS/MS
Targeted analysis of caffeine and paraxanthine		 Evaluation of the performance and hematocrit independence of the hemaPEN as a volumetric dried blood spot collection device [15]
Advanced dried samples
(Capitainer B Vanadate)		 MeOH:H2O (80:20, v/v)
0.01% formic acid		 UPLC–MS/MS
Targeted analysis of caffeine and paraxanthine		 Evaluation of the Capitainer-B microfluidic device as a new hematocrit-independent alternative for dried blood spot collection [16]
Traditional dried samples
(DBS)
Advanced dried samples
(VAMS)
Passive separation devices
(Telimmune, formerly Noviplex plasma prep cards)		 H2O HILIC–UPLC–FLR
Glycoprofiling		 Comparison of self-sampling blood collection for N-glycosylation analysis [17]
Whole biofluid collectors
(MSW2)		 5 µL IS: ACN/MeOH (70:30, v/v)
+100 µL MeOH		 LC–MS/MS
Targeted analysis of carbamazepine (and 7 of its metabolites), lamotrigine, phenytoin antiepileptic drugs		 Quantitative and qualitative application of a novel capillary microsampling device, Microsampling Wing™ (MSW), using antiepileptic drugs in rats [18]

1.1. Advanced Dried Microsamples

Advanced dried microsamples represent the largest portion of published literature in the scope of metabolic phenotyping, for which metabolite coverage and metabolite stability have been the key focus. A variety of devices of this technology type have been used, with findings detailed below. Some studies additionally performed analysis of haematocrit to identify the ability of the devices to perform haematocrit-independent sample collection, which are also detailed below, separately. A summary of applications of advanced dried microsamples in metabolic phenotyping has also been provided below.

1.1.1. Metabolite Coverage and Stability of Different Advanced Dried Microsample Devices

The Capitainer B-Vanadate (Capitainer AB; Solna, Sweden) was used for the quantification of caffeine and paraxanthine using venous blood from a healthy, caffeine-abstinent female (n = 1) [16]. This research was limited to simulated blood collections using venous blood transferred to the microsampling device with a micropipette, and stability testing was performed in triplicate for each of the two time points. Caffeine and paraxanthine concentrations were examined as stable at 4 days (60 °C) and at 3 months (room temperature or −20 °C).
The VAMS Mitra (Neoteryx; Torrance, CA, USA) device has been employed on two published accounts in metabolic phenotyping for the assessment of metabolite coverage and stability. In a study performed by Kok et al., the 10 µL VAMS Mitra (Neoteryx; Torrance, CA, USA) device was assessed using reversed-phase chromatography and hydrophilic interaction liquid chromatography on plasma taken from one individual [12]. From the two methodologies, 36 analytes were recovered (24 amino acids and 12 organic acids) and assessed for stability over 15 days, with measures being repeated at 2 h, and 1, 4, 7, and 15 days. No stability issues were observed during the first four days when stored at room temperature. However, at 15 days, increases and decreases were observed in some amino acids (a decrease of 19.2% and 15.4%, respectively, for methionine and tyrosine). These changes were seen more markedly in organic acids, highlighting that they are less stable than amino acids. Specifically, these were malic acid (−21.8%), glutathione (−31.8%), uric acid (−22.9%), glyoxylic acid (+36.8%), pyruvic acid (+26.0%), 3-hydroxypropionic acid (+37.9), and succinic acid (+46.3%). These stability findings contrast the known instability of organic acids and amino acids stored in liquid blood for the same time [12]. In another study, an untargeted methodology was used by Volani et al., to analyse an unknown number of pooled EDTA venous blood samples using 69 VAMS Mitra (Neoteryx) devices. The results of this study contrasted those of Kok et al., who demonstrated that metabolites were stable for up to 4 days when stored at room temperature [13]. Specifically, differences were observed in ~75% (77/103) putatively identified metabolites at 2 h, 24 to 48 h, and 4 days, when visualised with principal component analysis (PCA), and in fact up to 6 months (one-way ANOVA, p < 0.005), with 36% of these differences in metabolites manifesting as decreases over time (e.g., histidine, glutamine, and asparagine), and 31% of these metabolites increasing over time (e.g., glutamic acid, glyceric acid, and methionine) [13]. Reasons for metabolite increases were not postulated, but could potentially include the conversion of one metabolite to another or a relative concentration effect.
In a study of 20 healthy participants, the hemaPEN (Trajan; Melbourne, VIC, Australia) was used to collect blood samples for the analysis of 13 metabolites influenced by exercise, at training intervals, including nine amino acids and four organic acids [14]. Nine metabolite concentrations had variation <15% upon storage at −20 °C for five months. However, concentration decreases were observed in 2-oxoglutaric acid (35.1%) and methionine (56.5%), and concentrations increased for creatine (17.6%), and taurine (15.7%) [14].
Venous-derived serum, cited as the World Anti-Doping Agency (WADA) gold standard for sample collection in steroid profiling, was compared to a variety of advanced dried microsamples by Salamin et al. [11]. The researchers analysed 11 free and 8 conjugated steroids following testosterone gel administration in 14 healthy, eumenorrheic women. Testosterone concentrations were first measured in traditional DBSs (created with venous blood) and were found to highly correlate with those in serum (r > 0.84 with Passing–Bablok regression analysis). Following these positive results, Salamin et al. also investigated the TASSO-M20 (HemoLink; Seattle, WA, USA) (placed on the upper arm) for steroid profiles using capillary blood from 14 healthy volunteers (7 females, 7 males) compared to traditional Whatman Protein Saver Card DBSs (Cytiva; Marlborough, MA, USA) (via finger prick). Finger pick DBSs and TASSO-M20 (HemoLink; Seattle, WA, USA) dried samples demonstrated strong correlations for all quantified steroids using a Spearman’s correlation. These steroids included testosterone (r = 0.97, p < 0.0001), androstenedione (r = 0.85, p < 0.0001), DHEA (r = 0.93, p < 0.0001), 17a-OH-progesterone (r = 0.98, p < 0.0001), progesterone (r = 0.93, p < 0.01), and cortisol (r = 0.91, p < 0.0001). The researchers caveat their capillary DBS findings with the likelihood of high capillary testosterone concentrations being attributed to the topical application of the testosterone gel, although the use of advanced devices such as the TASSO-M20 (HemoLink; Seattle, WA, USA) can overcome this pitfall due to its independence from the sample collection site (i.e., can be applied to upper arm) [11][19]. Furthermore, Salamin et al. highlight the potential for DBS storage of steroids for 1 and 3 weeks at room temperature with minimal risk of degradation (contrary to serum samples) [11]. This translation is important for future applications of microsamples in the context of anti-doping (i.e., WADA) [11][19].

1.1.2. Analysis of Haematocrit

Evaluation of hct across studies using advanced dried microsamples has shown promising results. Results by Velghe et al. demonstrated the ability of the Capitainer B-Vanadate (Capitainer AB; Solna, Sweden) device to eliminate hct bias over a range of 18.8–55.0 [16]. Linear regression of Capitainer samples revealed that caffeine and paraxanthine were not affected by hct when compared to whole blood. Interestingly, when repeating the analysis using a traditional DBS sub-punch (again, simulated with a micropipette from venous blood), linear regression revealed the presence of a negative hct bias. Here, there were reduced analyte concentrations witnessed in DBSs compared to whole blood [16]. Accurate and precise collection of samples collected from a single drop of blood with minimal influence by hematocrit is similarly demonstrated by the hemaPEN (Trajan; Melbourne, VIC, Australia) [14][15][20]. This has been examined in the scope of analytical chemistry for caffeine and paraxanthine, where comparing hemaPEN (Trajan; Melbourne, VIC, Austrtalia) concentrations and those in whole blood revealed a 6.90% and 5.40% difference in mean concentrations, respectively [15]. Although statistically significant, the researchers concluded that this variation was negligible, when comparing hemaPEN (Trajan; Melbourne, VIC, Australia) to conventional sub-punch DBS results, and sub-punch DBS results to whole blood. In the therapeutic drug monitoring of fluoxetine and sertraline (and their metabolites), hemaPEN (Trajan; Melbourne, Victoria, Australia) samples revealed a range of 3–9% variation in analyte recovery for different values of hct [20]. Conversely, other advanced dried sample technologies purported (and marketed) to overcome the ‘haematocrit effect’, such as the VAMS Mitra (Neoteryx; Torrance, CA, USA), are yet to investigate hct in metabolic phenotyping workflows.

1.1.3. Summary of Advanced Dried Microsample Findings

Generally, the metabolic phenotyping of advanced dried microsamples performed analysis within a few days of microsample collection, with infrequent longitudinal stability assessment, up to a maximum of six months [12]. This is in contrast to traditional DBSs, which have been performed up to 21 years following hct correction (a factor that may not be necessary for the future use of archived advanced samples that are hct-independent) [21]. Storage conditions typically employed those readily available in laboratory settings (room temperature, −20 °C, −80 °C), which may not reflect the reality of multi-site clinical studies, where microsamples are likely to undergo temperature variations or may encounter extreme climates (high heat and humidity) [22]. Pre-analytical workflows for these samples mainly relied on manufacturer protocols and demonstrated great variability in drying time and analytical turnaround. Furthermore, 3/6 studies were limited to an n = 1 [11][12][16], with only one study reaching an n = 20 [14], which further research should seek to expand upon to increase the statistical power and ensure the reproducibility of results.

1.2. Passive Separation Devices

Passive separation devices with the ability to obtain blood fractions such as plasma and sera currently represent the area of greatest focus in microsampling technology innovation. Many plasma separation devices are yet to be made commercially available, including the Capitainer DPS (Capitainer AB; Solna, Sweden), Book-Type DPS Card (Q2 Solutions; Morrisville, NC, USA), and the HemaXis DX (DBS System SA; Gland, Switzerland), and thus have yet to be translated to metabolic phenotyping.
Telimmune plasma cards (formerly Noviplex plasma prep cards) are currently the only plasma separation technology that has been translated to metabolic phenotyping research [17]. This was performed by Cvetko et al., who compared the glycoprofiles of 10 participants. Their study utilised self-sampling, to compare Telimmune plasma cards (Novilytic), DBSs, and the VAMS Mitra (Neoteryx) devices to plasma. Both Telimmune plasma cards (Novilytic) and the VAMS Mitra (Neoteryx) devices managed to adequately replicate venous-derived plasma glycoprofiles. Their comparability was calculated using the relative deviance between the advanced microsampling devices and traditional DBSs, to plasma, for each of the 39 glycan peaks. Interestingly, the Telimmune plasma cards (Novilytic) had the least relative deviance to plasma (0.069), followed by VAMS Mitra (Neoteryx) (0.092), with the most deviance to plasma being displayed in traditional DBS samples (0.674).
A smaller subset of five participants was utilised to perform self-sampling in hexaplicate to assess the analytical reproducibility of different microsample platforms. These were calculated as average %CV (coefficient of variation) for all glycan peaks. Telimmune plasma cards (Novilytic) displayed the least variation (4.831%), followed by the VAMS Mitra (Neoteryx) (7.098%), with the most variation being seen in DBSs (14.305%). Cvetko et al. demonstrated the ability of passive plasma separation devices such as the Telimmune plasma cards (Novilytic) to show reproducibility with venous-derived plasma, and analytical reproducibility when sampling is self-administered by participants, thus highlighting the great potential for the use of this advanced microsampling technology in larger cohort studies [17]. Importantly, although acknowledged as a limitation, variations in sample volume and hct from self-sampling are factors that were not addressed in this research. Furthermore, haemolysis (from inappropriate capillary lancing techniques) and under-sampling by participants may interfere with mass spectrometry-based metabolic phenotyping pipelines, introducing erroneous results [17].
The only serum passive separation device in existence is the HemaSpot SE (Spot On Sciences; San Francisco, CA, USA). Currently, applications are limited to manufacturer publications, highlighting a gap in the translation of serum separating technologies to metabolic phenotyping [23].

1.3. Whole Biofluid Collectors

Presently, only one reported account of whole biofluid collection technology has been used in metabolic phenotyping [18]. This was a pre-clinical study performed using the MSW2 (Shimadzu; Kyoto, Japan) by Hotta et al., who investigated the pharmacokinetic effects of administering a cocktail of antiepileptic drugs (carbamazepine, lamotrigine, and phenytoin) in four rats, and performed qualitative metabolite identification in rats (n = 2) using carbamazepine (100 mg/kg) as a model drug. In the pharmacokinetic study, blood samples were collected via the tail vein, using a glass capillary to inoculate the device with blood before centrifuging to obtain the respective separated blood fragment ‘chips’. Drug stability testing was then performed for a variety of storage conditions on the different chips. The stability data of the drugs revealed percent biases within the acceptable range (≤±15.0%) for all conditions. In a pre-clinical context, this highlights the stability of these antiepileptic drug samples over a wide range of storage conditions. These conditions included plasma at ambient temperature for 24 h, whole blood at ambient temperature for 1 h, and plasma at −20 °C for 140 days (the total storage duration from sample collection to analysis). Additionally, freeze/thaw (−20 °C/ambient temperature) was investigated for two cycles, as were processed plasma samples at 6 °C for 42 h.
For the latter metabolite identification study, blood samples were collected at 1, 2, 4, 8, and 24 h post-carbamazepine dose. The contents of two device ‘chips’ at the same time points were pooled in a tube (to total 5.6 µL of plasma). This plasma was then diluted 10-fold with blank rat plasma. Following this, LC–MS/MS analysis allowed for the characterisation of seven metabolites of carbamazepine. This allowed for the development of a proposed metabolic pathway of carbamazepine in rat plasma. Hotta et al. concluded that the MSW2 (Shimadzu; Kyoto, Japan) is technically easy to use with minimal training, and their findings suggest that whole biofluid collection technologies can be useful for the assessment of metabolite safety and to perform metabolite identification [18].

2. Considerations for Future Application of Advanced Microsampling in Metabolic Phenotyping Workflows

The use of advanced microsampling devices (of each technology type) demonstrated within the literature has shown promise for bridging the gap in the routine adoption of microsamples in metabolic phenotyping and within clinical research. However, there remain crucial shortcomings associated with microsample collections that need to be addressed. For the successful metabolic phenotyping of microsamples, crucial additional validation steps are required yet often not always considered within the scope of the assessed literature. Dependent on the analysis, targeted or untargeted standardised methods can be used [9][24][25]. However, prior to achieving this, the assessment of device accuracy, optimal extraction method, and optimal temperature and storage conditions would need to be performed prior to their implementation, routinely. Additionally, the stability of the sample is another important factor as different conditions can affect the recovery of metabolites in a sample. Considerations for stability are further discussed in this section, with particular focus given to the shortcomings of assessments of the long-term storage and stability of advanced microsamples, pre-analytical reproducibility in microsample preparation, and analyte concentration variations between whole blood, plasma, and serum. This is needed before the successful identification, validation, and quantification of novel biomarkers can occur. Indeed, if microsampling is going to be successful in population-based metabolic phenotyping research, patient self-variability/error is an additional area of contention that needs to be addressed. These are important considerations for the future adoption of advanced microsampling devices in longitudinal metabolic phenotyping studies with regular participant follow-up.

2.1. Microsample Collection and Stability

DBSs have traditionally been used in a variety of applications due to their ability to provide a low-volume, fast, and minimally invasive collection. In fact, multiple protocols and guidelines exist for the collection of traditional dried microsamples (i.e., DBSs), such as those for the proper sampling of capillary blood [26], selection of filter paper [27], sample application to the carrier [27], and packaging and transport of samples [28], thus ensuring some level of sampling reproducibility in current applications of DBSs in the field of metabolic phenotyping. However, these sampling protocols do not apply to advanced microsamples, which are limited to manufacturer SOPs. A research gap therefore exists in assessing optimal collection protocols that consider different drying times, temperatures, and storage conditions, which is inherently linked to microsample stability.
Assessment of the long-term stability and optimal storage conditions of advanced devices is still necessary for best practice in metabolic phenotyping pipelines. Accurate detection of metabolite concentrations in biological samples naturally requires sample stability, as fluctuations in environmental conditions, such as temperature and humidity, may have a deleterious effect on biological samples in the pre-analytical phase [29][30]. For example, plasma and serum samples are known to be affected by storage conditions such as increased temperatures and repeated freeze/thaw cycles [18]. Microsamples are no different, and thus considerations from the literature have included ensuring airtight and leak-proof packaging with desiccant to prevent deterioration from heat and moisture accumulation, as well as providing protocols for temperate conditions during transport (in multi-site studies), and timeframes for sample turnaround [13][31].
Recent microsample applications have implemented the use of data loggers (EL-USB2+; Lascar Electronics, Erie, PA, USA) to monitor the temperature, dew point, and humidity of DBS samples during shipment. In one study, temperature and humidity were measured at 30 min intervals in DBS samples that travelled 11,600 miles in six days [29][30]. Samples were observed to range from below freezing to over 25 °C throughout the course of the journey. Although the compound analysed by Bowen et al. was not disclosed, the results of their in-house stability assessment (based on temperatures that their DBSs encountered previously) were performed by setting the low QC level to −20 °C and high QC level to 40 °C. Taken together, the study by Bowen et al. indicated that temperature extremes had no deleterious effects on the stability of a single compound in analytical replicates, with %CV ≤ 8% on average, following ambient storage for 4 months, and at −20 °C and 40 °C storage for 48 h [30][32].
Another advantage of dried sample matrices is enhanced metabolite stability over a range of temperatures during storage and transportation. Most research concludes that analytes appear to be stable (for a variety of matrices) when stored in conditions with a low temperature and humidity (~30%, akin to a lyophilised environment) [33]. Indeed, in a study by Strnadová et al., who examined the long-term stability of amino acids and acylcarnitines, it was found that, of the analysed amino acids, valine was stable for up to 14 years in DBS samples stored at room temperature. However, other amino acids and acylcarnitines degraded more rapidly ‘per year’ [34]. In 2017, similar results were demonstrated in a lipidomic analysis of air-dried DBS cards stored with desiccants at different temperatures (4 °C to 37 °C) by Gao et al. [35]. Significant changes were noted for diacylglycerides in cards stored at 4 °C and room temperature for up to two weeks, and in most lipids stored at 37 °C. In addition, in 2017, Drolet et al. attributed increased variation in 350 DBS metabolites to storage temperatures up to 37 °C in comparison to those at room temperature and −20 °C [36]. The highest level of variability that Drolet et al. observed was at 37 °C on Day 14 of the storage conditions [36]. Future research must consider translating these findings to advanced microsampling devices, as this is yet to be performed beyond temperatures commonly encountered in a laboratory setting (−80 °C, −20 °C, 4 °C, and room temperature).
A variety of procedures and environmental conditions have been investigated for their impact on the metabolite stability of advanced microsamples. However, in the current literature, most research has not provided meaningful conclusions relevant to long-term storage outcomes for metabolites. For instance, ‘variation’ in metabolites (classes, panels, or even individually) was often reported without an associated direction (increase or decrease). Additionally, analytical reference points for assessing stability were often weak, and time points spanned relatively short periods (or were infrequent if conducted over a longer time). The interpretations of these stability data were inconsistent, mainly focusing on analyte recoveries and concentrations for the classification of disease outcomes. Future research should seek to identify the reproducibility of purported metabolite stabilities based on storage duration and temperature. Furthermore, these should be over a greater period, with more frequent time points. This will allow for meaningful comparisons between studies, and address the limited research published on advanced microsampling devices’ long-term stability.

2.2. Microsample Preparation

Consistent microsample preparation including sub-punches (if using traditional DBSs), extraction, and automation is important for reproducible analyte extraction. Similarly, consistent enrichment is important, should detection limits need to be improved for quantification (preconcentration) and the selective removal of interfering substances (sample clean-up) [37]. These processes are integral in metabolic phenotyping workflows as they determine the molecular concentrations in complex biological matrices (including blood, urine, and body tissues) used to derive clinical and biological conclusions. As such, sample preparation optimisation (specific to the advanced microsample device in use, i.e., advanced dried microsample, passive separation device, or whole biofluid collector) is vital for the accurate interpretation of results.
In DBS microsample preparation workflows, a sub-punch (also referred to as a partial punch), usually 3 mm in diameter, is taken. Theoretically, this is to reduce volumetric inaccuracies by using a fixed-diameter punch, similar to taking a fixed volume of plasma with a pipette. However, the literature disagrees on the usefulness of this, largely due to sources of error related to the potential effect of hct and punch location bias when the whole spot is not analysed. Importantly, the size of DBS sub-punches can also artificially inflate values of metabolites compared to venous blood (when DBSs are simulated with venous blood as a result of inaccurate sample delivery) [38]. Accuracy and precision can be improved by including the fixed or individual hct values into the models that relate plasma and blood concentrations of analytes [39]. Following a sub-punch, the microsample is then ready for extraction. In metabolic phenotyping, extractions are normally performed using a variety of organic solvents to perform a protein precipitation prior to metabolic phenotyping [13][40][41][42].
The move towards automating pre-analytical processes, such as microsample preparation, has the potential to improve throughput and decrease manual error in metabolic phenotyping pipelines. In fact, this consideration has recently been adopted in the design of advanced microsampling devices such as the VAMS (Neoteryx; Torrance, CA, USA), which are also available in 96-well plate format (VAMS 96-Autorack) (Neoteryx; Torrance, CA, USA) [43]. Such designs have the potential for the automation of extraction in advanced microsampling device metabolic phenotyping pipelines; however, this kind of use is yet to be reported on a large scale. Future applications of advanced microsampling devices in automated metabolic phenotyping workflows offer the potential to conduct large epidemiological studies. Derivatisation is often used in metabolic phenotyping pipelines to improve analyte volatility (i.e., in GC) and analytical sensitivity [44]. However, it may affect the detection and reproducibility of certain classes of metabolites and hamper the detection of compounds [5]. Currently, derivatisation has been automated for fatty acid analysis of the phospholipid fraction of human plasma, and, to date, has been successfully applied to more than 28,000 samples of the InterAct project, measuring plasma–phospholipid profiles in over 12,000 diabetes cases and over 16,000 sub-cohort participants as part of a cancer study [44]. Scales of this size have not yet been achieved in automated workflows for the metabolic phenotyping of microsamples.

2.3. Determining an Equivalent Concentration Factor

The type of blood collection performed to obtain a microsample (venous, arterial, or capillary) will influence the metabolite content of the sample [26][27]. These differences mainly arise between capillary and venous samples (e.g., when comparing their sera or plasma). Differences are primarily due to capillary samples being derived from a dermal puncture, which results in contamination of venous blood with arterial blood, interstitial fluid, and intracellular fluid [26]. One study comparing capillary and venepuncture samples found that spot size is the most significant factor when applying a correction formula to account for differences in metabolite concentrations [45]. The formula derived by the researchers was
Y = f ( X ,   T ,   H )
which calculates the estimated standard value (Y) from DBS treatment (X), the treatment conditions such as temperature, humidity, drying time, shipment time, spot size (T), and donor characteristics such as health, age, and sex (H) reflecting potential interaction effects [45]. Use of newly developed microsampling devices that collect a fixed volume of blood may eliminate this consideration of spot size and punching [15].
Recently, correlations between DBS and dried serum spot (DSS) samples for the analysis of 25-hydroxyvitamin D (25OHD) were performed using LC–MS/MS technology [46]. Karvaly and colleagues observed that the mean biases showed no correlation with the 25OHD levels between DBS and DSS matrices (r ≤ 0.0671) [46]. In another study, DBS HbA1c (commonly used for diabetes monitoring) demonstrated over 95% correlation with standard venous samples; however, results from DBS cards older than 7 days had to be adjusted, indicating that metabolite concentrations will vary between sampling sites and matrices on which microsamples are collected [47]. Bridging studies should ideally be performed to validate that the concentrations obtained from capillary microsamples are equivalent to venous whole blood. Specifically, research is needed in the use of anticoagulants in venepuncture collections compared to advanced microsamples where anticoagulants are not included [33]. This is to overcome analytical and physiological issues associated with miniaturised volumes and complex collection matrices, which demand method development to focus on robust extraction procedures and the selection of sensitive analytical platforms.

2.4. Self-Sampling

The bulk of the literature on the metabolic phenotyping of microsamples has utilised samples collected prior to the study (excess/remnant samples) or has utilised trained personnel to collect microsamples (primarily DBSs). Alternatively, pipelines that adopt self-sampling offer the potential for improved participant accessibility and retention in research studies. Such patient-centric benefits are exemplified by the established use of blood glucose monitoring devices by diabetics [48]. However, the routine adoption of self-sampling (with advanced microsampling devices) is currently limited to a few accounts, with only a single published application in metabolic phenotyping [17]. Currently, only Cvetko et al. have investigated the use of self-sampling for dried samples (DBS) (VAMS) (Neoteryx; Torrance, CA, USA) and a passive plasma separation device (Telimmune plasma cards) (Novilytic; West Lafayette, IN, USA) for metabolic phenotyping [17]. They reported issues in undersampling, hct variation, and haemolysis. Importantly, this study acknowledged its relatively small sample size (n = 10), who were ‘familiar with self-sampling procedures’, for which only five participants participated in sample collection reproducibility testing. Interestingly, the reproducibility testing required the collection of hexaplicates for each of the three devices (i.e., 18 microsamples collected per participant in a single sitting), which potentially introduced repeated lancing pain. Furthermore, this account does not specify if these findings were device-specific [17]. Beyond metabolic phenotyping, advanced microsampling devices such as the HemaSpot HF (Spot on Sciences; San Francisco, CA, USA) have shown promising results in improving health engagement in men with HIV-1 [48]. This study had a return rate of 75.5%, with 418/554 enrolled participants returning a microsample. Of those, 80.6% (337/418 participants) returned kits that contained enough blood for testing. Of those who received a kit, interestingly, 49 required a second kit due to losing the kit or difficulties collecting blood (11 of whom attributed this to problems with the lancet because of calloused fingertips). Kocher et al. concluded that home collection with these devices for the assessment of viral load could be utilised as a monitoring tool between clinical visits for patients who struggle with antiretroviral therapy adherence [48]. Since the COVID-19 pandemic, the general public are much more adapted to home sampling, albeit with immunoassay kits, but the practice of self-sampling is now more accepted for health monitoring in the home. Advanced microsampling devices have the potential to leverage this familiarity of self-sampling for future research applications in the home setting.

This entry is adapted from the peer-reviewed paper 10.3390/separations9070175

References

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